The present invention relates in general to tire pressure monitoring systems in automobiles, and, more specifically, to automatically learning to associate transmissions from identified wheel-mounted sensors with the wheel locations where the respective wheels are installed.
Monitoring of tire pressure provides a useful safety feature as the result of being able to automatically inform a driver when a low pressure appears in any tire. A typical tire pressure monitoring system (TPMS) sensor unit includes a battery-powered device that is remotely mounted on each respective wheel. Pressure data from a transducer is wirelessly transmitted (e.g., via RF) to a vehicle-mounted receiver for analyzing the transmitted messages and to associate the measurements with respective wheel locations. Each sensor unit includes a unique identifier or serial number which is included in each message that the receiver can learn to associate with respective wheel positions so that the location of a tire experiencing low pressure can be reported to the driver. Because of the possibility of tire rotation (i.e., swapping of wheel locations for evening out the tire wear) or the replacement of a tire with a spare or new tire on a different wheel, these associations must be continually re-learned during vehicle operation.
One technique for learning the wheel location for data obtained from wheel-mounted TPMS sensors involves time correlation between data received from the TPMS sensors with data received from an anti-lock brake system (ABS) that directly monitors wheel positions. More specifically, the sensor unit may include an orientation sensor such as an accelerometer in order to time the broadcasting of messages according to a particular rotational position of the wheel, such as at the top of a wheel rotation. By triggering the transmission of messages once per wheel rotation, the timing of a string of tire pressure messages can be compared with direct measurements from each of the wheel locations in the ABS data. Since tire slippage, vehicle trajectory, and other factors result in differential overall rotation between the individual wheels, timing information from each respective TP MS sensor unit eventually matches only one of the sets of corresponding ABS data measurements. This general technique for learning the wheel associations is shown in U.S. Pat. No. 7,336,161 to Walraet, U.S. Pat. No. 8,528,393 to Craig et al., and U.S. patent application publication 2014/0019035 to Fink et al., for example.
In order to reliably associate the TPMS sensor ID's with respective wheel locations within a reasonable period of time, a high accuracy of the measured time of occurrence for each TPMS sensor message is needed. In an electrical architecture wherein the TPMS data and ABS data is received and processed by the same microprocessor or microcontroller based on a single timing reference (e.g., clock), synchronization between the data sets and the overall accuracy of the timing data itself is fairly straightforward to obtain. In a typical electrical architecture of an automotive vehicle, however, a distributed architecture is employed wherein the RF receiving and decoding circuits are located in one module and the processing (e.g., comparison) of the TPMS data with the ABS data in order to find the wheel associations is performed by circuits located in a different module (e.g., a body control module). The detection and decoding of the RF messages from the TPMS sensors in the RF module may take an amount of time that varies from one message to another. When the decoded messages are repackaged and sent to the other module doing the comparison, the time at which the message arrives at the comparing module is not sufficiently accurate for purposes of the comparison because of the variable delay between the time that the sensor unit was at the reference position and the time that the comparing module receives the corresponding message. Walraet '161 discloses a shared clock signal generated in one module and coupled directly to other modules for use in detecting the times for the TPMS data and ABS data. However, the dedicated provisioning of wiring for sharing a clock signal is undesirable.
Synchronization of separate clock references with different modules over existing communication lines (such as a multiplex bus) has provided limited accuracy due to bus delays for the associated messages and due to clock drift that continues to occur between synchronization messages. Therefore, improved timing measurements are needed in the context of a distributed processing system.